城郊景观动态模型研究──以沈阳市东陵区为例

基于渗透理论、马尔柯夫过程理论,采用中性模型方法,建立了3个不同的城郊景观动态模型,模型中分别介入不同自然因子和决策因子.利用模型对研究区景观进行了动态变化模拟.对模拟结果进行了评价,评价方法与指标包括:1)多分辨率拟合分析;2)最近邻概率;3)斑块大小和数目.结果发现,综合介入决策因素和自然因子的模型具有最好的效果.     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统(SES)模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现:(1)多数模型对景观过程与社会经济决策的反馈关系分析不足;(2)应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;(3)通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括:集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

壤中流模型研究的现状及存在问题

对国内外壤中流模型与模拟进行了较为系统的介绍,并针对这些模型提出了一种壤中流模型分类的方法,即根据模型所依据的主要原理将壤中流模型分为三大类：１）Ｒｉｃｈａｒｄｓ模型;２）动力波模型;３）贮水泄流模型．Ｒｉｃｈａｒｄｓ模型又可分为一维Ｒｉｃｈａｒｄｓ模型、二维Ｒｉｃｈａｒｄｓ模型和三维Ｒｉｃｈａｒｄｓ模型;贮水泄流模型又可分为动力贮水泄流模型和Ｂｏｕｓｉｎｅｓｑ贮水泄流模型．同时,将这３类模型进行对比,指出了它们各自优点和不足．     

玉米生长生理生态学模拟模型

建立了玉米（Ｚｅａ ｍａｙｓ Ｌ．）生长生理生态学模拟模型（ＭＰＥＳＭ）。模型由５个子模型组成：１）气象资料形成子模型,２）水分影响子模型,３）氮素影响子模型,４）玉米发育子模型,５）玉米生长子模型。ＭＰＥＳＭ具有５个主要功能：１）模拟环境条件（气象因子、土壤湿度和氮素供应）对玉米生长和发育的影响,２）模拟玉米的发育进程,３）模拟玉米的生长过程,４）模拟玉米产量形成过程,５）玉米最优化栽培管理决策     

卧龙自然保护区大熊猫生境破碎化研究

生境破碎化主要有两个方面,一是形态（景观结构）上的破碎化;二是生态功能上的破碎化,将景观连接度的概念引入卧龙自然保护区大熊猫的生境评价研究中,通过选择影响大熊猫生存的３种典型景观因子;地形高度,地形坡度和食物来源,从生态功能上研究３种景观因子由于空间组合的不匹配而形成的生境破碎化现状,在生物各景观因子重要性的基础上进行景观连接模糊相对赋值,并建立景观连接度评价模型,在地理信息系统支持下,研究了卧龙     

基于景观生态风险评价的涪江流域景观格局优化

以流域为尺度进行景观生态风险评价以及景观格局优化,有利于为流域生态系统服务的提高和人类活动管控提供科学依据。以涪江流域为研究区域,从"自然-社会-景观格局"3个维度选取10个评价因子建立评价指标体系,采取空间主成分分析法（SPCA）对流域景观生态风险进行综合评价,再基于生态风险评价的结果和生态源地利用最小累积阻力模型（MCR）和网络分析等方法实现流域景观格局优化。研究结果表明：①涪江流域景观生态风险等级在空间分布上呈西北部高于东南部地区,主要是受自然和景观格局因子影响较大。②涪江流域所面临的生态风险问题较为严重,生态风险等级为中度及以上的区域面积总和为25596.51 km²,占研究区总面积比例的65.35%。③生态源地以林地和水域为主,面积为11194.28 km²,占流域总面积比例为25.58%。④构建生态廊道共41条,总长度为5229.04 km,其中原有廊道29条,新添廊道12条,提取生态节点53个;利用网络分析形成了以主廊道为"中轴",构建的生态廊道为"辅助",提取的生态节点为"枢纽"的较为完整的网络生态结构。对研究区景观格局优化前后的连通度进行对比,优化后的整体景观格局连通度得到较大幅度提升。     

作物生长模拟模型参数校正与有效化的理论和实践

以ＧＯＳＳＹＭ 模型为例系统阐述了作物生长模拟模型有关参数校正和模型有效化的一般原理和方法,同时用新疆棉区的试验具体校正了品种参数、土壤参数和修改了部分模块,并对校正结果进行了验证．结果表明,两个试点土壤２０ ～４０ 、６０ ～８０ｃｍ 两个土层生长季水分动态观测值与土壤参数校正后模型的模拟值吻合较好;系５ 品种试验生育期６ 项生物指标动态模拟结果与实测值拟合的相关系数都在０ ．９ 以上,并且不同栽培条件的３ 个处理的模拟产量的相对误差平均为７ ．５ ％ ,模拟结果较理想．     

空间直观景观模型的验证方法

空间直观景观模型已是当前景观生态学研究的一大热点。空间景观模型模拟空间格局变化。其模拟结果包含非空间数据和空间数据。空间直观景观模型的验证除进行非空间数据的验证外,还需要进行空间数据的验证。本文回顾了空间直观模型发展历程,总结现有的空间直观模型验证方法。包括主观评价、图形比较、偏差分析、回归分析、假设检验、多尺度拟合度分析和景观指数分析,同时提出今后空间直观景观模型验证方法研究的重点方向。     

基于不确定性的城市扩展用地生态适宜性评价

针对土地适宜性评价中采用定性和单因素方法而产生的主观、片面和精度低等缺点,分析了土地适宜性评价中的不确定性因素,并提出基于不确定性和灰色系统关联度的土地生态适宜性评价模型。结合长沙市生态规划,在野外生态调查的基础上,依据稳定性、独立性、主导性和综合性原则选取坡度、地基承载力、土壤生产力、植被、土壤渗透性、地表水、居民点用地程度、景观价值等土地生态适宜性评价因子。在地理信息系统（GIS）和遥感（Rs）的支持下,定量地获取各评价因子信息;采用AHP法确定各评价因子的权重;并运用改进的土地生态适宜性评价模型对长沙市城市扩展用地进行定性定量的评价,最终得到长沙市最适宜用地、适宜用地、基本适宜用地、不适宜用地、不可用地分别占总用地的14．77％、16．64％、24．07％、30．91％、13．61％,说明长沙市适宜开发的土地较多（最适宜、适宜和基本适宜用地占55．48％）。根据评价结果提出的相应对策,对城市用地的可持续性发展具有指导意义。     

基于斑块尺度的云南省景观生态安全时空演变及归因

随着人类活动的需求增强和范围扩张,建设开发、农林种植等行为不断侵占和切分原有土地系统,导致区域景观斑块小型化和破碎化,产生了诸多生态问题。云南既是我国西南生态安全屏障,同时也是生态环境比较脆弱敏感的地区,研究云南省景观生态安全及其影响因素,对区域、国家乃至国际生态安全具有重要意义。以云南省为研究对象,提出基于斑块尺度的景观生态安全评价方法评估云南省1990-2018年近三十年的景观生态安全,采用空间自相关方法分析其时空演变规律,并通过地理探测器识别影响景观生态安全的主要驱动因子。结果表明：（1）与传统生态安全评价模型相比,斑块尺度的景观生态安全模型对区域内部景观生态安全的变化更敏感,能够反映区域内部多年景观生态安全状况的细微变化。（2）云南省1990-2018年整体生态安全成本成波动上升趋势,其中"三屏两带"地区的景观生态安全状况提升明显,而滇中城市群外围以及滇东南喀斯特地带的部分区域有所下降。云南省景观生态安全的Global Moran''s I指数平均值为-0.293,区域内部斑块破碎,空间分布上具有离散性。（3）云南省景观生态安全状况目前主要受到人口密度、年平均温度和海拔高度的影响;从交互驱动方面看,云南省景观生态安全空间分布是自然和人文因子共同作用的结果,其中自然因子的交互驱动作用更明显。研究结果可为云南省景观生态安全格局优化提供理论支撑与科学依据。     

医疗机构合理用药评价模型的构建研究

目的 针对医疗机构的合理用药水平进行评价研究。方法 根据医疗机构合理用药的具体要求,构建医疗机构合理用药评价指标体系,采用基于模糊群决策的方法和多指标评价分析法构建医疗机构合理用药评价模型。结果 构建了基于模糊群决策的医疗机构合理用药评价模型,并通过实例分析证明了评价模型的可行性。结论 建立的基于模糊群决策的医疗机构合理用药评价模型能够对医疗机构的合理用药水平进行科学评价,为提高医疗机构合理用药水平奠定基础。     

互信息理论结合决策树算法的土壤质量预测

在充分利用土壤类型、土地利用方式、岩性类型、地形、道路、工业类型等影响土壤质量主要因素,准确获取区域土壤质量的空间分布特征的基础上,采用互信息理论对13个辅助变量（岩性类型、土地利用方式、土壤类型、到城镇的距离、到道路的距离、到工业用地的距离、到河流的距离、相对高程、坡度、坡向、平向曲率、纵向曲率和切线曲率）进行筛选,然后通过决策树See5.0预测研究区土壤质量.结果表明： 影响研究区土壤质量的主要因素包括土壤类型、土地利用方式、岩性类型、到城镇的距离、到水域的距离、相对高程、到道路的距离和到工业用地的距离;以互信息理论选取的因子为预测变量的决策树模型精度明显优于以全部因子为预测变量的决策树模型,在前者的决策树模型中,无论是决策树还是决策规则,分类预测精度均达到80%以上.互信息理论结合决策树的方法在充分利用连续型和字符型数据的基础上,不仅精简了一般决策树算法的输入参数,而且能有效地预测和评价区域土壤质量等级.     

The human toxicity potential and a Strategy for Evaluating Model Performance in Life Cycle Impact Assessment

The Human Toxicity Potential (HTP) is a quantita tive toxic equivalency potential (TEP) that has been introduced previously to express the potential harm of a unit of chemical released into the environment. HTP includes both inherent toxicity and generic source-to-dose relationships for pollutant emissions. Three issues associated with the use of HTP in Life Cycle Impact Assessment (LCIA) are evaluated here. First is the use of regional multimedia models to define source-to-dose relationships for the HTP. Second is uncertainty and variability in sourceto-dose calculations. And third is model performance evaluation for TEP models. Using the HTP as a case study, we consider important sources of uncertainty/variability in the development of source-to-dose models — including parameter variability/uncertainty, model uncertainty, and decision rule uncertainty. Once sources of uncertainty are made explicit, a model performance evaluation is appropriate and useful and thus introduced. Model performance evaluation can illustrate the relative value of increasing model complexity, assembling more data, and/or providing a more explicit representation of uncertainty. This work reveals that an understanding of the uncertainty in TEPs as well as a model performance evaluation are needed to a) refine and target the assessment process and b) improve decision making.     

Decision Analysis Utilizing Data from Multiple Life-Cycle Impact Assessment Methods Part I: A Theoretical Basis

Numerous methodologies for the life-cycle impact assessment (LCIA) step of life-cycle assessment (LCA) are currently in popular use. These methods, which are based on a single method or level of analysis, are limited to the environmental fates, impact categories, damage functions, and stressors included in the method or model. Because of this, it has been suggested within the LCA community that LCIA data from multiple methods and/or levels of analysis, that is, end-point and midpoint indicators, be used in LCA-based decision analysis to facilitate better or, at least more informed, decision making. In this (two-part) series of articles, we develop and present a series of LCA-based decision analysis models, based on multiattribute value theory (MAVT), which utilize data from multiple LCIA methods and/or levels of analysis. The key to accomplishing this is the recognition of what LCIA damage indicators represent with respect to decision analysis, namely, decision attributes and, in most cases, proxy attributes. The use of proxy attributes in a decision model, however, poses certain challenges, such as the assessment of decision-maker preferences for actual consequences that are only known imprecisely because of inherent limits of both LCA and scientific knowledge. In this article (part I), we provide a brief overview of MAVT and examine some of the decision-theoretic issues and implications of current LCIA methods. We illustrate the application of MAVT to develop a decision model utilizing damage indicators from a single LCIA methodology; and, we identify the decision-theoretic issues that arise when attempting to combine LCIA indicators from multiple methods and/or levels of analysis in a single decision model. Finally, we introduce the use in our methodology of constructed attributes to combine related end-point damage indicators into single decision attributes and the concept and evaluation of proxy attributes.     

基于可变模糊识别模型的海水环境质量评价

海水环境质量评价可以视为是一个具有确定性的评价指标和评价标准与具有不确定性的评价因子及其含量变化相结合的分析过程,各评价指标含量具有中介过渡性,构建了基于可变模糊集合理论的海水环境质量评价方法(简称“VFEM”),并以莱州湾海水水质为研究对象,应用提出的可变模糊评价模型对该区域海水水质状况进行评价,实例应用表明,该模型通过可变模型参数变化(a,p),以线性模型与非线性模型相结合,将最后稳定结果作为海洋水质环境的最后评价结果,从而确定水质评价等级,评价结果更为可信,同时该模型通过级别特征值精确区分各水质采样点的水质优劣,对各水质采样点的水质级别有更准确的定位,为海水环境质量综合评价提供了一种合理而适用的方法.     

Stroke Treatment Prediction Using Features Selection Methods and Machine Learning Classifiers

ObjectivesFeature selection in data sets is an important task allowing to alleviate various machine learning and data mining issues. The main objectives of a feature selection method consist on building simpler and more understandable classifier models in order to improve the data mining and processing performances. Therefore, a comparative evaluation of the Chi-square method, recursive feature elimination method, and tree-based method (using Random Forest) used on the three common machine learning methods (K-Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed to select the most relevant primitives from a large set of attributes. Furthermore, determining the most suitable couple (i.e., feature selection method-machine learning method) that provides the best performance is performed.Materials and methodsIn this paper, an overview of the most common feature selection techniques is first provided: the Chi-Square method, the Recursive Feature Elimination method (RFE) and the tree-based method (using Random Forest). A comparative evaluation of the improvement (brought by such feature selection methods) to the three common machine learning methods (K- Nearest Neighbor, naïve Bayesian classifier and decision tree classifier) are performed. For evaluation purposes, the following measures: micro-F1, accuracy and root mean square error are used on the stroke disease data set.ResultsThe obtained results show that the proposed approach (i.e., Tree Based Method using Random Forest, TBM-RF, decision tree classifier, DTC) provides accuracy higher than 85%, F1-score higher than 88%, thus, better than the KNN and NB using the Chi-Square, RFE and TBM-RF methods.ConclusionThis study shows that the couple - Tree Based Method using Random Forest (TBM-RF) decision tree classifier successfully and efficiently contributes to find the most relevant features and to predict and classify patient suffering of stroke disease.”     

Two-dimensional grey cloud clustering-fuzzy entropy comprehensive assessment model for river health evaluation

Abstract

The river health evaluation is typically complex non-linear system with characteristics of fuzziness and randomness. However, conventional gray clustering method has difficult to effectively describe fuzzy and random information simultaneously. For this purpose, the cloud model and fuzzy entropy theory are introduced to establish 2D gray cloud clustering-fuzzy entropy comprehensive evaluation model. Different with health level models, it reflects river health situation from aspects of health level and corresponding water body complexity simultaneously. The health level is obtained by gray cloud whitened weight function (first sub-system) and fuzzy entropy represents complexity and fuzziness of river health situation (second sub-system). Moreover, multi-level river health evaluation indicator system is constructed with dividing indicators into common and distinct sections according to differences on river characteristics. Meanwhile, indicator weights are determined by renewed combined weighting method based on minimum deviation principle. Finally, we conduct health evaluation work for rivers in the Taihu basin. The evaluation health levels and fuzzy entropy for river A–G are H3 (0.4888, relatively significant); H2 (0.5476, relatively fuzzy); H2 (0.7526, fuzzy); H2 (0.4731, relatively significant); H2 (05138, relatively fuzzy); H3 (0.5822, relatively fuzzy), and H2 (0.4064, relatively significant), respectively. Results are consistent with current river health situation and more intuitive than compared models. Furthermore, evaluation results with four different weighting methods are compared to further demonstrate rationality of the weighting method and evaluation model. Hence, the model proposed is demonstrated to provide new insight for solving river health assessment problem effectively.     

多目标决策在小豆种质资源评价中的应用

基于熵权的多目标决策分析模型,结合模糊数学和熵的思想,运用多属性决策分析中的双基点法,对小豆种质进行定量评价.经综合评价,8个小豆种质材料从优到劣的顺序为B00766、B01805、B00091、B00651、B01670、B00655、B00388、B00774,避免了由单一性状指标来判断参试材料优劣所造成的偏差,可为育种亲本优选提供理论依据.结果表明,多目标决策评价模型在少量小豆种质资源的评价中运算简便,易于掌握.     

Genetic screening and genetic counseling: Knowledge,attitudes, and practices in two groups of family planning professionals

Abstract

Genetic counseling has evolved from a eugenic orientation to an orientation concerned with the physical and mental well‐being of counselees. This change in genetic counseling, which has received formal recognition in a new definition of genetic counseling, requires collateral development of the processes and evaluation of the outcomes of counseling. This paper offers a theory of genetic counseling which interrelates genetic information, psychological responses, learning theory, and decision‐making. The theory presented for genetic counseling is based on the more general theories of learning, decision‐making, and adaptation to psychological stress. This theory is extended into a practical model that provides a comprehensive explanation of the relationships between the activities of genetic counseling and informed decision‐making, which is assumed to be a major element of healthy counselee adjustment. Implications of this theory for the genetic counselor, the counselee, and the assessment of clinical and program success are discussed.